No abstract

According to this study, because of its light weight, high specific strength, and stiffness at high temperatures, Al6061 is the most appropriate material in the transportation business. The major goal of this research is to evaluate the physical properties of Al6061, such as thermal conductivity and electrical resistivity, by experimental investigation utilizing the multivolt drop approach. As Artificial Intelligence techniques become more widespread, they are being used to forecast material properties in engineering research. So, the second goal of this research is to employ Artificial Neural Networks to build a prediction model with fewer errors by utilizing experimental data. It will reduce the situation of direct observations throughout a wide range of temperatures where the physical properties of Al6061 are significant. As a consequence, it was discovered that the enhanced optimum ANN has significant mechanical properties that impact prediction. The anticipated results in electrical resistivity and thermal conductivity had Root Mean Squared Errors of 0.99966 and 0.99401, respectively, with R-Square average values of 0.820105. Various tests and ANN methodologies were used to validate and compare the suggested results. The comparison of predicted values with multivolt drop experimental results demonstrated that the projected ANN model provided efficient Al6061 accuracy qualities.